Skill UI

This guide demonstrates how to build advanced AI agents using Anthropic's Tool Use (Function Calling) feature. It allows Claude to dynamically determine when external actions are needed, such as querying databases, calling APIs, or reading files. The pattern involves sending the user query, receiving a `tool_use` request, executing the corresponding function in your code, and submitting the result back to Claude for a final, coherent response. Essential for building multi-step, agentic workflows.

A logging wrapper designed to instrument and monitor calls to the Claude API. It automatically tracks critical metrics including token usage, API latency, estimated cost, and error details. Essential for implementing robust observability patterns and setting up proactive alerts in production environments.

This skill provides advanced sentiment analysis tailored for financial news and market texts. It supports two modes: high-speed, localized analysis using FinBERT, and advanced, reasoning-based analysis via LLM. Users can determine the sentiment polarity (positive, negative, or neutral), score, and specific reason for financial content, making it ideal for market intelligence and data enrichment.

This tool collects comprehensive diagnostic evidence for troubleshooting Anthropic Claude API issues. It gathers critical data such as request IDs, rate limit headers, SDK versions, and connection details. It is essential for generating accurate support tickets or diagnosing persistent API failures.

This skill establishes a comprehensive CI/CD pipeline specifically for Abridge clinical AI integrations using GitHub Actions. It enforces critical healthcare compliance steps, including FHIR R4 resource validation, robust detection of Protected Health Information (PHI) leaks (like SSN), and end-to-end sandbox integration testing. Ideal for deploying secure, compliant AI applications in the healthcare sector.

A comprehensive troubleshooting guide for Abridge clinical documentation integration. Covers common errors in authentication, EHR connectivity, audio streaming, note generation, and FHIR validation. Essential for developers and technical users debugging AI-powered clinical workflows.

This workflow implements Abridge's end-to-end clinical documentation pipeline. It captures ambient audio from patient encounters, performs real-time transcription, utilizes generative AI to structure the medical notes (e.g., SOAP, H&P), generates required medical codes (ICD-10, CPT), and finally inserts the structured data directly into the EHR system. Designed for primary clinical encounters, minimizing manual charting time.

This workflow converts complex clinical encounter notes into plain-language, patient-facing After-Visit Summaries (AVS). It structures key information such as diagnosis, next steps, and medications. The system supports multi-language translation and facilitates pushing the final, readable summary directly to patient portals using FHIR standards.

This minimal working example demonstrates Abridge's ambient AI clinical documentation capabilities. It simulates a complete patient encounter session, allowing users to submit transcripts and receive a highly structured clinical note. Ideal for testing EHR connectivity, verifying AI workflow, or learning how structured medical data (SOAP notes, ICD/CPT codes) is automatically generated from free-text conversation.

This guide details the process of setting up authentication and integration credentials for the Abridge clinical AI platform. It covers prerequisites for connecting to major EHR systems (Epic, Athena) using FHIR R4 APIs and implements OAuth 2.0 (SMART on FHIR) flows, ensuring secure, HIPAA-compliant data handling for ambient documentation.

A comprehensive readiness checklist for deploying clinical AI systems, such as Abridge, within regulated healthcare environments. It covers critical aspects including HIPAA compliance, Business Associate Agreements (BAA), EHR integration validation (FHIR R4), robust infrastructure security (TLS 1.3, secrets management), and provides structured scripts for pre-launch validation and post-launch rollback planning.

Provides a comprehensive architectural blueprint for integrating ambient AI solutions (Abridge) into multi-site health systems. It guides the full process from ambient audio capture to structured note generation, covering data flow, component design, FHIR R4 standards, HIPAA compliance, and integration patterns with major EHRs like Epic and Athena.